R_23 - Machine Learning - Important Questions Unit wise

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5 Marks questions

UNIT–I

Introduction to Machine Learning

Most Important 5-Mark Questions

  1. Define Machine Learning and explain its applications.
  2. Explain the evolution of Machine Learning with examples.
  3. Explain learning by rote, learning by induction, and reinforcement learning.
  4. Describe the stages in the Machine Learning process with a neat diagram.
  5. Explain data acquisition and list various data sources.
  6. What is feature engineering? Explain its importance in ML.
  7. Explain different types of data used in Machine Learning.
  8. Describe data representation techniques in ML.
  9. Explain model selection and model learning.
  10. Explain model evaluation techniques and accuracy testing.
  11. What is search and learning in Machine Learning?
  12. Explain the concept of datasets (training, testing, validation).

Very High Probability:
Stages of ML, Feature Engineering, Model Evaluation, Data Acquisition

UNIT–II

Nearest Neighbor–Based Models

Most Important 5-Mark Questions

  1. Explain proximity measures used in nearest neighbor models.
  2. Describe Euclidean, Manhattan, and Minkowski distance measures.
  3. Explain non-metric similarity functions with examples.
  4. Explain proximity between binary patterns (Hamming distance).
  5. Explain the K-Nearest Neighbor (KNN) algorithm with steps.
  6. Discuss the effect of value of k on KNN performance.
  7. Explain Radius Nearest Neighbor algorithm.
  8. Differentiate between KNN classification and KNN regression.
  9. Discuss the performance issues of KNN classifiers.
  10. Explain methods to improve KNN performance.
  11. Compare distance measures and similarity measures.
  12. Explain curse of dimensionality in nearest neighbor models.

Very High Probability:
KNN algorithm, Distance measures, Performance of KNN

UNIT–III

Decision Trees & Bayesian Models

Most Important 5-Mark Questions

  1. Explain decision trees for classification with a diagram.
  2. Explain entropy and information gain.
  3. Explain Gini index and its role in decision trees.
  4. Explain the steps involved in decision tree construction.
  5. What is overfitting in decision trees? How is it controlled?
  6. Explain the bias–variance trade-off.
  7. Explain Random Forest algorithm and its advantages.
  8. Explain Bayes theorem and its application in classification.
  9. Explain the Naive Bayes classifier and its assumptions.
  10. Explain class conditional independence in Naive Bayes.
  11. Explain Bayes classifier optimality.
  12. Compare Decision Tree and Naive Bayes classifier.

Very High Probability:
Entropy & IG, Naive Bayes, Bias–Variance trade-off

UNIT–IV

Linear Discriminants & Neural Models

Most Important 5-Mark Questions

  1. Define linear discriminant function and explain classification.
  2. Explain linear separability with examples.
  3. Explain the Perceptron model and its limitations.
  4. Explain the Perceptron Learning Algorithm.
  5. Explain Support Vector Machines (SVM).
  6. Explain the concept of margin in SVM.
  7. Explain kernel trick and its need.
  8. Explain logistic regression and its applications.
  9. Explain linear vs non-linear classification.
  10. Explain Multi-Layer Perceptron (MLP) architecture.
  11. Explain the Backpropagation algorithm.
  12. Compare Perceptron and MLP.

Very High Probability:
Perceptron algorithm, SVM & Kernel Trick, Backpropagation

UNIT–V

Clustering

Most Important 5-Mark Questions

  1. Define clustering and explain its applications.
  2. Explain partitioning of data in clustering.
  3. Explain the K-Means clustering algorithm with steps.
  4. Discuss the advantages and limitations of K-Means.
  5. Explain Agglomerative clustering with example.
  6. Differentiate between Agglomerative and Divisive clustering.
  7. Explain hard clustering vs soft clustering.
  8. Explain Fuzzy C-Means clustering.
  9. Explain Rough K-Means clustering algorithm.
  10. Explain Expectation Maximization (EM) based clustering.
  11. Explain spectral clustering.
  12. Explain cluster validity measures.

Very High Probability:
K-Means, Hierarchical clustering, Fuzzy clustering, EM clustering

EXAM STRATEGY (IMPORTANT)

  • Prepare 6–8 questions per unit → guaranteed coverage
  • Focus on:
    • Algorithms + steps
    • Definitions + diagrams
    • Advantages & limitations
  • Write answers in:
    • Definition (2 lines)
    • Explanation (points)
    • Diagram / formula (if any)

2 Marks Questions

 UNIT–I

Introduction to Machine Learning

Important 2-Mark Questions

  1. Define Machine Learning.
  2. What is supervised learning?
  3. What is unsupervised learning?
  4. Define reinforcement learning.
  5. What is learning by induction?
  6. Define data acquisition.
  7. What is feature engineering?
  8. Define data representation.
  9. What is model selection?
  10. Define model learning.
  11. What is model evaluation?
  12. Define accuracy in ML.
  13. What is model prediction?
  14. What is search space in ML?
  15. What is a dataset?

⭐ Very High Probability:
Machine Learning definition, Feature Engineering, Accuracy, Dataset types

UNIT–II

Nearest Neighbor–Based Models

Important 2-Mark Questions

  1. Define proximity measure.
  2. What is Euclidean distance?
  3. Define Manhattan distance.
  4. What is Minkowski distance?
  5. What is a similarity measure?
  6. Define Hamming distance.
  7. What is K-Nearest Neighbor (KNN)?
  8. Define value of k in KNN.
  9. What is KNN regression?
  10. What is Radius Nearest Neighbor?
  11. What is curse of dimensionality?
  12. What is non-metric similarity?
  13. Define binary pattern.
  14. What is distance-based classification?
  15. Define weighted KNN.

⭐ Very High Probability:
Distance measures, KNN definition, Curse of dimensionality

UNIT–III

Decision Trees & Bayesian Models

Important 2-Mark Questions

  1. What is a decision tree?
  2. Define entropy.
  3. What is information gain?
  4. Define Gini index.
  5. What is impurity measure?
  6. What is overfitting?
  7. Define pruning.
  8. What is bias–variance trade-off?
  9. What is a Random Forest?
  10. Define Bayes theorem.
  11. What is Naive Bayes classifier?
  12. What is class conditional independence?
  13. Define prior probability.
  14. What is posterior probability?
  15. What is zero-frequency problem?

⭐ Very High Probability:
Entropy, Information Gain, Naive Bayes, Overfitting

UNIT–IV

Linear Discriminants & Neural Models

Important 2-Mark Questions

  1. Define linear discriminant function.
  2. What is linear separability?
  3. Define Perceptron.
  4. What is an activation function?
  5. Define learning rate.
  6. What is Support Vector Machine (SVM)?
  7. Define margin in SVM.
  8. What is a kernel function?
  9. What is kernel trick?
  10. Define logistic regression.
  11. What is Multi-Layer Perceptron (MLP)?
  12. What is backpropagation?
  13. Define gradient descent.
  14. What is sigmoid function?
  15. What is ReLU?

⭐ Very High Probability:
Perceptron, SVM, Kernel Trick, Backpropagation

UNIT–V

Clustering

Important 2-Mark Questions

  1. Define clustering.
  2. What is partitioning of data?
  3. Define K-Means clustering.
  4. What is a centroid?
  5. Define hierarchical clustering.
  6. What is agglomerative clustering?
  7. What is divisive clustering?
  8. Define hard clustering.
  9. Define soft clustering.
  10. What is Fuzzy C-Means clustering?
  11. What is Rough clustering?
  12. Define EM clustering.
  13. What is spectral clustering?
  14. What is cluster validity?
  15. What is Elbow method?

⭐ Very High Probability:
K-Means, Hierarchical clustering, Fuzzy clustering, EM

LAST-MINUTE EXAM STRATEGY (2-MARKS)

Learn definitions + formulas + keywords
Answer in 1–2 crisp lines
Use proper ML terminology
Avoid examples unless asked


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